T. Cui et al. presented an inverse metasurface design method named Reactive which is capable of recognizing the internal principles between a unit-cell structure and its S-parameters with an average accuracy of 76.5%[1]}. Zhang et al. have proposed a machine learning method for designing anisotropic coding metasurfaces with a link between deep learning and BPSO to explore the ideal reflection phases of two-unit cells for the desired target[2]}. Shan et al. have introduced a deep convolutional neural network to code the programmable metasurface for multi-beam steering, with an average accuracy of more than 94%[3]}. A metasurface inverse design method using a machine learning method has been provided, in[4]} which is capable of directly computing the output metasurface structure by entering the sought design targets into the model.
Recently, a double deep Q-learning network (DDQN) has been introduced to distinguish the ideal material type and optimize a hologram structure to increase its efficiency[5]}.
